DNNs have demonstrated remarkable success in solving complex prediction tasks. However, recent works [1]}, [2]}, [3]} have shown that they are particularly vulnerable to adversarial examples, which are in the form of small perturbations to inputs but can lead DNNs to predict incorrect outputs. Adversarial examples are commonly generated through an iterative optimization procedure, which resembles the iterative sampling procedure of SGLD in Eq. REF . GMMC (and JEM) further utilizes sampled data along with real data for model training (see Eq. REF ). This again shares some similarity with adversarial training [2]}, which has been proved to be the most effective method for adversarial defense. In this section, we show that GMMC achieves considerable robustness compared to other methods thanks to the MMC modeling [5]} and its generative training.
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